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-# 🎋🌿 **Production-Ready ULMA Instruction Fine-Tuning of Meta LLaMA 3.1 8B Project** 🌿🎉  
-*Note: If you are a company training your own language models or a developer practicing fine-tuning, you can adopt this approach to align your model with human preferences effectively. This project incorporates a robust pipeline designed to prevent your model from generating responses that are racist, hateful, or socially harmful.* 
-            🚀 Clone this Repository and boom!
+# 🎋🌿 **Production-Ready Instruction Fine-Tuning of Meta LLaMA 3.2 3B Instruct Project** 🌿🎉  
+updating soon: 
 
 ## **Problem Statement**  
 ---  
-*Note: This project simulates an industry-standard scenario where I am pretending to work at XYZ Company. The LLaMA 3.1 8B model was deployed in production on our product but was generating harmful outputs such as hate speech, sexually explicit, and racially biased content. This created an immediate need for instruct-based ULMA fine-tuning. As a developer, I approached this problem using industry-standard practices to ensure the model aligns with societal norms and ethical standards while meeting business requirements.*
+*Note: This project simulates an industry-standard scenario where I am assuming the role of a developer at XYZ Company. The LLaMA 3.2 (3B) model has been successfully deployed in production as part of our product. However, to better serve our large user base of Kannada speakers, fine-tuning the model on a Kannada-specific dataset has become essential.*  
 
-**High-Level Note:** All detailed information about **Instruction Fine-Tuning**, **ULMA**, concepts like **reward models**, **training of large language models**, **Reinforcement Learning from Human Feedback (RLHF)**, **Direct Preference Optimization**, **Parameter-Efficient Fine-Tuning**, **Higher Floating-Point Precision Conversion to Lower Precision**, **Quantization**, **LoRA**, and **QLoRA** have already been covered in the `docs/` folder of this repository. Please refer to the documentation there for an in-depth understanding of how these concepts work. In this readme, I will go with project pipeline etc or the main details relevant to the project.
+
+
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+**High-Level Note:** All detailed information about **Instruction Fine-Tuning**, concepts like **reward models**, **training of large language models**, **Reinforcement Learning from Human Feedback (RLHF)**, **Direct Preference Optimization**, **Parameter-Efficient Fine-Tuning**, **Higher Floating-Point Precision Conversion to Lower Precision**, **Quantization**, **LoRA**, and **QLoRA** have already been covered in the `docs/` folder of this repository. Please refer to the documentation there for an in-depth understanding of how these concepts work. In this readme, I will go with project pipeline etc or the main details relevant to the project.
 
 
 ---  
 
-At **XYZ Company**, we adopted the **LLaMA 3.1 8B model** as the core **AI foundation for our product**. However, recent **performance evaluations in production environments** exposed critical limitations. These issues led to **customer dissatisfaction** and highlighted the following challenges:  
+# 🌟 **Fine-Tuning LLaMA 3.2 3B for Kannada Language Adaptation** 🌟  
+
+At **XYZ Company**, we adopted the **LLaMA 3.2 (3B) model** as the core **AI foundation for our product** to provide cutting-edge AI-driven solutions. However, due to our **large Kannada-speaking user base**, the model required fine-tuning to better cater to their needs. After analyzing its performance, our **manager decided** that fine-tuning on a Kannada-specific dataset was essential to enhance the model’s effectiveness.
+
+---
+
+# 🌟 **Fine-Tuning LLaMA 3.2 3B for Kannada Language Adaptation** 🌟  
 
-### **Identified Issues**  
-- The model generated **racist text** and **sexual content** in certain scenarios.  
-- It produced **hate speech** targeting specific entities, causing potential reputational damage.  
-- A significant portion of the generated content was **hateful**, displayed **social norm biases**, and posed **safety risks** to customers.  
+At **XYZ Company**, we adopted the **LLaMA 3.2 (3B) model** as the core **AI foundation for our product** to provide cutting-edge AI-driven solutions. However, due to our **large Kannada-speaking user base**, the model required fine-tuning to better cater to their needs. After analyzing its performance, our **manager decided** that fine-tuning on a Kannada-specific dataset was essential to enhance the model’s effectiveness.
 
-### **Need for Immediate Action**  
-These challenges raised significant ethical and safety concerns due to the product's large user base. The **AI Systems Team**, under the guidance of management, hired **prompt engineers** to extensively test the model's responses. Despite their efforts, the results remained harmful to customers and society, necessitating a more robust solution.  
 
-### **Proposed Solution: ULMA Instruction Fine-Tuning**  
-To address these issues and ensure alignment with societal norms, ethical standards, and human preferences, the team decided to proceed with **instruction-based ULMA fine-tuning**. This solution aims to:  
-- Prevent hate speech and harmful content.  
-- Reduce biases in model outputs.  
-- Ensure safety and ethical compliance in real-world applications.  
+To achieve this, we are leveraging the **Hugging Face dataset** `charanhu/kannada-instruct-dataset-390k`, containing **390,000 high-quality rows of Kannada instructions**. This dataset serves as the foundation for fine-tuning the model to:  
+-  **Better understand Kannada**: Improve comprehension of the language’s syntax, semantics, and nuances.  
+-  **Generate accurate responses**: Ensure the model aligns with Kannada-speaking users' expectations and use cases.  
+-  **Enhance the overall user experience**: Build a model that feels intuitive and responsive to Kannada-related queries.  
 
-Fine-tuning is considered the **only viable method** to deeply align the model with societal and ethical expectations, ensuring it performs reliably and responsibly in production environments.  
 
-### **My Role as a Developer**  🎋
-As a developer, I am responsible for delivering a fine-tuned **LLaMA 3.1 8B** model that meets the defined Key Performance Indicator (KPI) objectives while aligning with societal and ethical standards.  
 
-- I will **instruct fine-tune** the model using high-quality **ULMA datasets** from **Anthropic's Hugging Face Dataset** (**Unified-Language-Model-Alignment/Anthropic_HH_Golden**). This process will improve the model’s responses, ensuring it does not generate hateful, sexual, or racist text and aligns with societal norms.  
 
-- Given the constraints of limited GPU resources, I will employ **QLoRA-based 4-bit precision quantization** using **BitsAndBytes**. This involves:  
-  - First **quantizing** the model to 4-bit precision.  
-  - Adapting **LoRA layers** to fine-tune the model within **Google Colab**, optimizing resource usage without sacrificing performance.  
+### **My Role as a Developer** 🎋  
 
-- Advanced prompting techniques and supervised instruction fine-tuning will be incorporated to maintain robust model performance while balancing potential accuracy trade-offs.  
+As a developer, I am responsible for delivering a fine-tuned **LLaMA 3.2 3B** model that aligns with the defined **Key Performance Indicator (KPI)** objectives and ensures exceptional performance for Kannada-speaking users.  
 
+- I will **instruct fine-tune** the model using the high-quality **Kannada dataset** from **Hugging Face** (`charanhu/kannada-instruct-dataset-390k`).  
 
-- This project operates under a **tight deadline**, necessitating a strong focus on efficiency and quality.  
+- To address the constraints of **limited GPU resources**, I will implement **QLoRA-based 4-bit precision quantization** using **BitsAndBytes**, which involves:  
+  - First **quantizing the model** to 4-bit precision to reduce computational overhead.  
+  - Adding **LoRA (Low-Rank Adaptation) layers** to fine-tune the model efficiently within **Google Colab**, ensuring optimal resource utilization without compromising performance.  
+
+- This project is being executed under a **tight deadline**, which requires a strategic focus on both **efficiency and quality**.  
+
+I will collaborate closely with the **AI Systems Team** and **prompt engineers** to ensure the fine-tuning process adheres to business objectives and meets user requirements.  
 
-Close collaboration with the **AI Systems Team** and **prompt engineers** will ensure the fine-tuning process meets business objectives and customer requirements, delivering a model that is reliable, ethical, and effective for real-world applications.  
 
 
 
 ---
-## Goals 🎉  
+### **Project Goals** 🎯  
+
+1. **Serve Our Large Kannada-Speaking Customer Base**  
+   - Adapt the **LLaMA 3.2 3B** model to effectively understand and respond to queries from our extensive Kannada-speaking audience, ensuring an improved and localized user experience.  
+
+2. **Enhance Kannada Language Understanding**  
+   - Fine-tune the model using the **Hugging Face Kannada dataset** (`charanhu/kannada-instruct-dataset-390k`), focusing on instruction-specific tasks to ensure accurate, context-aware, and culturally relevant responses in Kannada.  
+
+3. **Optimize Resource Utilization with Google Colab**  
+   - Use **Unsloth** to load the model and **QLoRA-based 4-bit precision quantization** for fine-tuning, leveraging **Google Colab** to minimize resource consumption while delivering high-quality results.  
+
 
-- **Mitigate harmful outputs** such as hate speech, sexually explicit content, and racial bias by instruct-based **ULMA fine-tuning**
-- Deliver a **production-ready solution** that aligns the model with societal norms, ethical standards, and customer expectations.  
-- **Impress customers** and maintain trust among our **large user base** by ensuring the model generates safe, inclusive, and appropriate content.  
-- Prioritize **GPU resource optimization** by leveraging **QLoRA** techniques, to efficiently fine-tune the model.
 
 
 ---
-## **My Approach** 🚀
+## **My Approach** 🚀  
+
+The **instruct-based fine-tuning** process will adhere to industry standards, ensuring the model is rigorously tested before being deployed for inference.  
 
-The **Instruct-based ULMA fine-tuning** process will be done as per industry standards, thorough testing will be done before using model for inference.
+### **Steps in My Approach**  
 
-I will begin by training the model using **Anthropic's HH Golden dataset** to align it with societal norms, human preferences, and to mitigate harmful content such as hate speech, racism, and hallucinations, while improving response accuracy.
+1. **Dataset Preparation**  
+   - Use the **Hugging Face Kannada dataset** (`charanhu/kannada-instruct-dataset-390k`) for training, focusing on enhancing the model's performance in understanding and responding to Kannada-specific queries.  
 
+2. **Efficient Training with Quantization**  
+   - Optimize the training process by implementing **4-bit precision quantization** using **QLoRA** for efficient resource utilization.  
+   - Leverage **Google Colab's limited GPU resources** to achieve faster training without compromising the quality of the fine-tuning process.  
 
-To optimize training efficiency, I will implement **quantization** and **4-bit precision** for faster training within **Google Colab**, utilizing limited GPU resources. 
+3. **Model Deployment and Feedback Loop**  
+   - Once the model is fine-tuned, it will be uploaded to an **S3 bucket** for easy access and deployment.  
 
-Additionally, I will apply **advanced prompt engineering techniques** to further guide the model’s responses, ensuring it aligns with human preferences and ethical standards, while addressing the key issues identified (e.g., hate speech, racism, and harmful content generation).
+This approach ensures a resource-efficient, scalable, and production-ready model tailored to meet the needs of Kannada-speaking users.  
 
-Once, model is finetuned It will be pushed to S3 Bucket, and used for inference to test and gather feedback.
 
 **Note: This is high-level view.**